"LLM-based code reviewers can mistakenly classify correct implementations as violating natural-language requirements."
Jin and Chen identify systematic overcorrection in language-model judgments of whether implementations satisfy specifications. Their research shows that an AI-based judge may itself introduce erroneous correctness assessments, reinforcing the distinction between adding a checker and establishing a trustworthy verification process.
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